pp(sys.version_info, 0)
# PARAMETERS ####
learning = False # Decide whether to run the sparse coding algorithm
classification = True # Run classification
resume = True
ts_size = 13 # size of the time surfaces
tau = 5000 # time constant for the construction of time surfaces
polarities = 1 # number of polarities that we will use in the dataset (1 because polarities are not informative in the cards dataset)
if resume:
output_path = "../../../results/main_nmnist.py.d1062928/"
else:
output_path = create_output_path()
dtr = None
dte = None
#pp("rank: ", comm.rank)
to_scatter_train = None
to_scatter_test = None
if comm.rank == 0:
#fh = tb.open_file("../datasets/nmnist_small.h5")
fh = tb.open_file("../../../datasets/nmnist_one_saccade.h5")
#fh = tb.open_file("../datasets/nmnist.h5")
dtr = [d.read().astype(np.int32) for d in fh.root.train]
dte = [d.read().astype(np.int32) for d in fh.root.test]
to_scatter_train = [dtr[i::nprocs] for i in range(nprocs)]
params = {}
exec(compile(open(param_fname).read(), param_fname, 'exec'), params)
# Extract some parameters
N = params.get('N', 5000) # no. of datapoint in the testset
size = params.get('size', 5) # width / height of bars images
p_bar = params.get('p_bar', 1. / size) # prob. for a bar to be active
D = params.get('D', size**2) # observed dimensionality
H = params.get('H', 2 * size) # latent dimensionality
model = params['model'] # the actual generative model
# Ground truth parameters -- only used for generation
params_gt = params.get('params_gt') # Ground truth param
# Create output path
output_path = create_output_path(param_fname)
# Disgnostic output
pprint("=" * 40)
pprint(" Running bars experiment (%d parallel processes)" % comm.size)
pprint(" size of training set: %d" % N)
pprint(" size of bars images: %d x %d" % (size, size))
pprint(" number of hiddens: %d" % H)
pprint(" saving results to: %s" % output_path)
pprint()
# Generate bars data
my_data = model.generate_data(params_gt, N // comm.size)
# Configure DataLogger
print_list = ('T', 'Q', 'pi', 'sigma', 'N', 'MAE')
# Extract some parameters
N = params.get('N', 5000) # no. of datapoint in the testset
N_train = int(0.9 * N)
N_test = N - N_train
size = params.get('size', 5) # width / height of bars images
p_bar = params.get('p_bar', 1. / size) # prob. for a bar to be active
D = params.get('D', size**2) # observed dimensionality
H = params.get('H', 2 * size) # latent dimensionality
model = params['model'] # the actual generative model
model_str = model.__class__.__name__
# Ground truth parameters -- only used for generation
params_gt = params.get('params_gt') # Ground truth param
# Create output path
output_path = create_output_path('learning-and-inference-' + param_fname)
# Disgnostic output
pprint("=" * 40)
pprint(" Running bars experiment (%d parallel processes)" % comm.size)
pprint(" size of training set: %d" % N)
pprint(" size of bars images: %d x %d" % (size, size))
pprint(" number of hiddens: %d" % H)
pprint(" saving results to: %s" % output_path)
pprint()
my_data = model.generate_data(params_gt, N_train // comm.size)
my_test_data = model.generate_data(params_gt, N_test // comm.size)
# Configure DataLogger
store_list = ('*')